As our understanding of network architecture and the emergent properties of nerve networks gets ever more detailed, the need for network models to explain and suggest experiments continues to grow. Dr. Harirngton has adapted a 64 electrode planar electrode array to record from the cerebral ganglia of snails. With the multi electrode array, Dr. Harrington and her students are recording the neural activity related to sensory processing in two different snail model systems. One of these models is the wolfsnail, a predatory snail that tracks its prey (other snails) by following their slime trails, detecting the slime with a unique, specialized sense organ. Dr. Harrington and her students have recorded vast amounts of data about the activity of the cerebral ganglia in the snails and changes that occur in that activity in response to slime and other stimuli applied to the sensory epithelia. Analyzing the data and understanding its significance is a major computational challenge requiring the tools of mathematical biology. The usefulness of the data as a tool for understanding neural processes will be greatly enhanced by using the data to inform the development of computational models of neural integration and decision-making processes. The data collected from wolfsnail ganglia and those of other snails are being analyzed in three ways: first with a spike sorting program that counts and correlates neural spike activity across all 64 electrodes to calculate and compare spike frequency and synchronization across the electrode array, A second approach uses cross-correlation to identify changing patterns of synchronized activity. A third approach will use an Independent Component Analysis (ICA) algorithm to decompose the activity recorded at the 64 electrodes and identify different source signals contributing to the total signal. This approach has been used for decomposition of evoked field potentials in human EEG and MEG applications, neural recording techniques that are emulated by our invertebrate recordings in many respects. The data collected with the electrode array will be correlated with the activity of individual cells recorded electrophysiologically in order to determine the contributions to the network activity attributable to the different types of cells in the ganglia. Combining data about the spatial and temporal pattern of neural activity across the ganglia with information about the biophysical propoerties of individual cells will enable us to develop network and computational models of sensory processing in an invertebrate model system.